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Language Skills

R
Bash
Git/GitHub
Python

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The long-form version of my CV is available as a pdf

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Last updated on 2021-03-11.

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David Zhang

I am a bioinformatician who focusses on data analysis, visualisation and method development. The goal of my work is to improve the molecular diagnosis rate of patients with rare disorders. For this purpose, I specialise in developing algorithms that integrate large-scale genomic and transcriptomic datasets in order to detect aberrant, disease-causing events.

Education

Research assistant, part-time PhD, Bioinformatics

University College London

London, UK

Present - 2017

  • Thesis: Using transcriptomics to improve the diagnosis rate of rare disease patients.
  • The goal of my PhD is to develop and apply software that improve the genetic diagnosis rates using RNA-sequencing.

MSc, Neuroscience

University College London

London, UK

2016 - 2015

  • Thesis: The role of mitochondrial dysfunction in Xerodoma pigmentosum
  • Grade: Merit (68%)
  • Awarded post-graduate support scheme bursary (£10,000)

BSc, Biomedical science

University College London

London, UK

2015 - 2012

  • Thesis: Investigating the function of CYFIP1 in the development of rat hippocampal neurons.
  • Grade: 2:1 (69%)

H.S.

Queen Elizabeth’s School

Barnet, UK

2012 - 2007

  • Grade: Maths (A*), Biology (A*), Chemistry (A*), Sociology (A).

Research Experience

Honorary Researcher (2 months)

Johns Hopkins Bloomberg School of Public Health

Remote

2020

  • In collaboration with Leonardo Collado-Torres, we used the recount3 dataset and LIBD samples to study the effect of complex splicing in individuals with neurological disease.

Research Technician

University College London

London, UK

2017 - 2016

  • Used R and bash to investigate the effect of genetic variation on the age of onset of dementia and cognition within Down syndrome patients.

Industry Experience

Bioinformatician internship (3 months)

Verge Genomics

Remote

2020

  • Detection of aberrant splicing events in complex disease patients.
  • Used AWS infrastructure to analyse 100s of RNA-seq samples derived from patients with Parkinson’s disease and amyotrophic lateral sclerosis

Software & programming

Bioconductor packages

N/A

N/A

Present - 2020

  • dasper: detection of aberrant splicing events in RNA-sequencing. Author and maintainer. 363 downloads.
  • megadepth: BigWig and BAM related utilities. An R wrapper for the megadepth software developed by Chris Wilks. Co-author and maintainer. 304 downloads.

Data science blog

N/A

N/A

2021

  • Published chess-related blog post on Medium. Post was curated by Towards Data Science and selected for their hands-on-tutorials column, which displays pieces that highlight best practices of data science.
  • Applied popular data science packages in python to analyse chess.com data.

Advanced R

N/A

N/A

2021 - 2020

Data wrangling

Neuroimmunology & CSF Laboratory, NHS

London, UK

2018 - 2016

  • Developer and maintainer of data wrangling pipelines that improved the efficiency and standardisation of monthly financial reports.

Teaching Experience

Developing Bioconductor Packages

University College London

Virtual Event

2020

Unit testing using testthat edition 3

rstats club

Virtual Event

2020

  • Talk regarding unit testing fundamentals, the importance of testing and new features released in the R package testthat edition 3.

R fundamentals

Clinician Coders

London, UK

2020 - 2018

  • Developed materials and lead workshops that aimed to teach R fundamentals to clinicians.

Selected Publications

Megadepth: efficient coverage quantification for BigWigs and BAMs

Bioinformatics

N/A

2021

  • Wilks C, Ahmed O, Baker DN, Zhang D, Collado-Torres L, Langmead B. 2021. Megadepth: efficient coverage quantification for BigWigs and BAMs. Bioinformatics.
  • Role: R package developer.
  • DOI: https://doi.org/10.1101/2020.12.17.423317

Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

Jama Neurology

N/A

2021

  • Kia DA, Zhang D, Guelfi S, Manzoni C, Hubbard L, United Kingdom Brain Expression Consortium (UKBEC), International Parkinson’s Disease Genomics Consortium (IPDGC), Reynolds RH, Botía JA, Ryten M, Ferrari R, Lewis PA, Williams N, Trabzuni D, Hardy J, Wood NW. 2021. Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes. Jama Neurology.
  • Role: Co-first author.
  • DOI: https://doi.org/10.1001/jamaneurol.2020.5257

Incomplete annotation of disease-associated genes is limiting our understanding of Mendelian and complex neurogenetic disorders.

Science advances

N/A

2020

  • Zhang D, Guelfi S, Ruiz SG, Costa B, Reynolds RH, D’Sa K, Liu W, Courtin T, Peterson A, Jaffe AE, Hardy J, Botia JA, Collado-Torres L and Ryten M. 2020. Incomplete annotation of disease-associated genes is limiting our understanding of Mendelian and complex neurogenetic disorders. Science Advances.
  • Role: First Author.
  • DOI: https://doi.org/10.1126/sciadv.aay8299